基于频繁模式挖掘的脑功能连通性分析。在小鼠模型表征中的应用

Aurélie Leborgne, F. Ber, Laetitia Degiorgis, L. Harsan, Stella Marc-Zwecker, V. Noblet
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引用次数: 2

摘要

功能磁共振成像(fMRI)是一种可以在体内探索大脑功能的成像技术。许多用于分析这些数据的方法都是基于图建模的,每个节点对应一个大脑区域,边缘表示它们的功能链接。这项工作的目的是研究在图中提取频繁模式的方法的兴趣,以比较两个种群之间的这些数据。结果是在阿尔茨海默病小鼠模型的特征与一组对照小鼠比较的背景下提出的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis Of Brain Functional Connectivity By Frequent Pattern Mining In Graphs. Application To The Characterization Of Murine Models
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique that allows to explore brain function in vivo. Many methods dedicated to analyzing these data are based on graph modeling, each node corresponding to a brain region and the edges representing their functional link. The objective of this work is to investigate the interest of methods for extracting frequent pattern in graphs to compare these data between two populations. Results are presented in the context of the characterization of a mouse model of Alzheimer’s disease in comparison with a group of control mice.
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